Learning by Augmenting Rules and Accumulating Censors
Author(s)Winston, Patrick H.
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This paper is a synthesis of several sets of ideas: ideas about learning from precedents and exercises, ideas about learning using near misses, ideas about generalizing if-then rules, and ideas about using censors to prevent procedure misapplication. The synthesis enables two extensions to an implemented system that solves problems involving precedents and exercises and that generates if-then rules as a byproduct . These extensions are as follows: If-then rules are augmented by unless conditions, creating augmented if-then rules. An augmented if-then rule is blocked whenever facts in hand directly demonstrate the truth of an unless condition, the rule is called a censor. Like ordinary augmented if-then rules, censors can be learned. Definition rules are introduced that facilitate graceful refinement. The definition rules are also augmented if-then rules. They work by virtue of unless entries that capture certain nuances of meaning different from those expressible by necessary conditions. Like ordinary augmented if-then rules, definition rules can be learned. The strength of the ideas is illustrated by way of representative experiments. All of these experiments have been performed with an implemented system.
learning, artificial intelligence, analogy